Optimizing Brain Tumor Recognition with Ensemble support Vector-based Local Coati Algorithm and CNN Feature Extraction

Optimizing Brain Tumor Recognition with Ensemble support Vector-based Local Coati Algorithm and CNN Feature Extraction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Optimizing Brain Tumor Recognition with Ensemble support Vector-based Local Coati Algorithm and CNN Feature Extraction A. Sumithra, Joe Prathap P M, Karthikeyan A, Dhanasekaran . S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3853111/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Nowadays, brain tumor (BT) recognition has become a common phenomenon in the healthcare industry. In the medical system,BT identification and classification can take a significant part in the diagnostics and considerations of the patients. BT is characterized as an abnormal mass of tissue in which the cells proliferate unexpectedly with no control over cell proliferation. In recent years, improvements in machine learning (ML), particularly deep learning (DL) procedures, have shown significant potential for mechanizing and improving these undertakings by utilizing medical imaging information. Also, we examine the difficulties and probabilities in this field, including information shortage, model interpretability, and moral contemplations. To overcome these challenges Ensemble support Vector-based Local Coati (ESV-LC) Algorithm is employed to identify and classify the brain tumor disease in the patients. For optimal classification, the features need to be extracted and this can be achieved by employing the Convolutional Neural network (CNN). To accurately classify BT, Ensemble Support Vector Machine (ESVM) is involved, which enhances classification performance, and hyperparameter tuning is performed through Local Search Coati Optimization. The Brain Tumor Image Dataset and Figshare Brain Tumor dataset are utilized for BT classification and identification. The performance metrics like Accuracy, Precision, Sensitivity, Specificity, and F1-score are to be evaluated, where the accuracy achieves the value of 98.3%, sensitivity of 97.6%, precision of 97.7%, specificity of 98.1%, and F1-score of 96.7% respectively. Brain Tumor Medical Images Deep Learning Ensemble Support Vector Machine Local Search Coati Optimization Classification Feature Extraction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3853111","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267050752,"identity":"707624d8-df37-4d37-ae9f-bf463f56bf54","order_by":0,"name":"A. 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In the medical system,BT identification and classification can take a significant part in the diagnostics and considerations of the patients. BT is characterized as an abnormal mass of tissue in which the cells proliferate unexpectedly with no control over cell proliferation. In recent years, improvements in machine learning (ML), particularly deep learning (DL) procedures, have shown significant potential for mechanizing and improving these undertakings by utilizing medical imaging information. Also, we examine the difficulties and probabilities in this field, including information shortage, model interpretability, and moral contemplations. To overcome these challenges Ensemble support Vector-based Local Coati (ESV-LC) Algorithm is employed to identify and classify the brain tumor disease in the patients. For optimal classification, the features need to be extracted and this can be achieved by employing the Convolutional Neural network (CNN). 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